A Survey of Techniques for Event Detection in Twitter

By Atefeh Farzindar, Wael Khreich. International Conference on Climate Informatics, 2015

749 citations recorded by the source corpus.

Subfields: Information Systems, Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/b0a8d512169453d92fbe44bc77a96bcc9cb9c85f

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Citations in the corpus

Query Expansion Techniques for Information Retrieval: a Survey (2017, neutral): “Some other recent applications of query expansion are plagiarism detection [197], event search [87, 15,41], text classification [262], patent retrieval [175,176,261], in dynamic process in IoT [120,121], classification of e-commerce [126], biomedical IR [1], enterprise search [170], code search [199”
Real-Time Detection of Traffic From Twitter Stream Analysis (2015, background): “In fact, SUMs are unstructured and irregular texts, it contains informal or shortened words, misspellings or grammatical errors [1].”
Facebook, Twitter and Google Plus for Breaking News: Is There a Winner? (2014, background): “Amongst researchers, Twitter is a very popular data source when finding breaking news, event detection and other interesting stories (see the survey (Atefeh and Khreich 2013)).”
Tweet Analysis for Real-Time Event Detection and Earthquake Reporting System Development (2013, background): “An event might have actively participating agents, passive factors, products, and a location in space/time [3].”
Location based services: ongoing evolution and research agenda (2018, background): “…applications for making (location) recommendations (Huang 2016; Bao et al. 2015), modelling individual and crowd mobility, understanding the dynamics and semantics of cities (Gao, Janowicz, and Couclelis 2017) and detecting and managing real-time events (e.g. disasters) (Atefeh and Khreich 2015).”
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation (2017, background): “Social media feeds are also important data streams for the identification of topical and trending events (see Atefeh & Khreich, 2015, for a recent review).”
Predicting Social Unrest Events with Hidden Markov Models Using GDELT (2017, background): “For a survey on these detection techniques in twitter, we point the readers to [18].”